SoroushVahidi/scidocs huggingface dataset:
Recognition of Car Makes and Models From a Single Traffic-Camera Image
(2015)

This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition.
The turing test track of the 2012 Mario AI Championship: Entries and evaluation
(2013)

The Turing Test Track of the Mario AI Championship focused on developing human-like controllers for a clone of the popular game Super Mario Bros. Competitors participated by submitting AI agents that imitate human playing style. This paper presents the rules of the competition, the software used, the voting interface, the scoring procedure, the submitted controllers and the recent results of the competition for the year 2012. We also discuss what can be learnt from this competition in terms of believability in platform games. The discussion is supported by a statistical analysis of behavioural similarities and differences among the agents, and between agents and humans. The paper is co-authored by the organizers of the competition (the first three authors) and the competitors.
A cross-national profile of bullying and victimization among adolescents in 40 countries
(2009)

(1) To compare the prevalence of bullying and victimization among boys and girls and by age in 40 countries. (2) In 6 countries, to compare rates of direct physical, direct verbal, and indirect bullying by gender, age, and country. Cross-sectional self-report surveys including items on bullying and being bullied were obtained from nationally representative samples of 11, 13 and 15 year old school children in 40 countries, N = 202,056. Six countries (N = 29,127 students) included questions about specific types of bullying (e. g., direct physical, direct verbal, indirect). Exposure to bullying varied across countries, with estimates ranging from 8.6% to 45.2% among boys, and from 4.8% to 35.8% among girls. Adolescents in Baltic countries reported higher rates of bullying and victimization, whereas northern European countries reported the lowest prevalence. Boys reported higher rates of bullying in all countries. Rates of victimization were higher for girls in 29 of 40 countries. Rates of victimization decreased by age in 30 of 40 (boys) and 25 of 39 (girls) countries. There are lessons to be learned from the current research conducted in countries where the prevalence is low that could be adapted for use in countries with higher prevalence.
The Flipped Classroom : An Opportunity To Engage Millennial Students Through Active Learning Strategies
(2013)

"Flipping" the classroom employs easy-to-use, readily accessihle technology in order to free class time from lecture. This allows for an expanded range of learning activities during class time. Using class time for active learning versus lecture provides opportunities for greater teacher-to-student mentoring, peer-to-peer collaboration and cross-disciplinary engagement. This review of literature addresses the challenges of engaging today's students in lecture-based classrooms and presents an argument for application of the "flipped classroom" model hy educators in the disciplines of family and consumer sciences.
A stepped-carrier 77-GHz OFDM MIMO radar system with 4 GHz bandwidth
(2015)

This paper presents a 77-GHz orthogonal frequency-division multiplexing (OFDM) multiple-input multiple-output (MIMO) radar system with 4 GHz bandwidth. Due to the limited bandwidth of the analog-to-digital and digital-to-analog converters a stepped-carrier approach is used. OFDM radar measurements with a bandwidth of 200MHz at 20 different carrier frequencies are combined in signal processing to achieve a high range resolution OFDM MIMO radar. Spectrally-interleaved OFDM signals are used to allow for simultaneous transmission and thus MIMO operation. The proposed approach is verified in practical measurements of a static target scenario and a slowly moving target scenario. Therefore a self-developed software defined radar evaluation platform with four transmitters and four receivers is used. The expected high range resolution could be achieved and range/angle/velocity measurements show the suitability for detecting slowly moving targets like pedestrians.
Machine Learning Paradigms for Speech Recognition: An Overview
(2013)

Automatic Speech Recognition (ASR) has historically been a driving force behind many machine learning (ML) techniques, including the ubiquitously used hidden Markov model, discriminative learning, structured sequence learning, Bayesian learning, and adaptive learning. Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems arising from the inherently sequential and dynamic nature of speech. On the other hand, even though ASR is available commercially for some applications, it is largely an unsolved problem - for almost all applications, the performance of ASR is not on par with human performance. New insight from modern ML methodology shows great promise to advance the state-of-the-art in ASR technology. This overview article provides readers with an overview of modern ML techniques as utilized in the current and as relevant to future ASR research and systems. The intent is to foster further cross-pollination between the ML and ASR communities than has occurred in the past. The article is organized according to the major ML paradigms that are either popular already or have potential for making significant contributions to ASR technology. The paradigms presented and elaborated in this overview include: generative and discriminative learning; supervised, unsupervised, semi-supervised, and active learning; adaptive and multi-task learning; and Bayesian learning. These learning paradigms are motivated and discussed in the context of ASR technology and applications. We finally present and analyze recent developments of deep learning and learning with sparse representations, focusing on their direct relevance to advancing ASR technology.
2CAuth: A New Two Factor Authentication Scheme Using QR-Code
(2013)

Password based schemes has been the standard means of authentication over decades. Enhancements use entities like ownership (something one possess), knowledge (something one knows), and inherence (something one is) as first factor and mobile phones as token less second factor, in combinations, to offer different levels of security assurances, trading off usability. In this paper we present ‘2CAuth’ a new two factor authentication scheme that enhances secure usage of application information and preserves usability, without sacrificing user’s privacy. A significant feature of the scheme is that it DOES NOT call for any synchronization between Mobile Network Operator (MNO) and users. The analysis of the scheme clearly brings out its effectiveness in terms of its usability even at times of peak loads on mobile networks. The scheme has the dual advantage of carrying out the verification of transactions which involve the physical presence of the user as well as those to be done in his absence.
Ultraflat Interleaved Triangular Current Mode (TCM) Single-Phase PFC Rectifier
(2014)

This paper presents the analysis and realization of a topology suitable to realize a power factor correction (PFC) rectifier with a thickness of only a few millimeters. The low height of the converter requires all components to be integrated into the printed circuit board (PCB). Still reasonable dimensions of the converter PCB are feasible (221 mm × 157 mm for a 200 W PFC rectifier), since PCB-integrated inductors and capacitors allow for high energy densities due to their large surface area which facilitates a low thermal resistance to ambient. A multicell totem-pole PFC rectifier employing a soft-switching modulation scheme over the complete mains period is identified as an adequate topology. The mode of operation is entitled triangular current mode (TCM) due to the triangular-shaped inductor currents. The modulation technique requires a reliable description of the switching transition of a half-bridge in order to provide accurate timing parameters. For this purpose, a simplified model of the nonlinear MOSFETs' output capacitances facilitates closed-form analytical expressions for duty cycle and switching frequency. Furthermore, this paper details the control of three interleaved converter cells which yields a reduction of the input current ripple. A 200 W TCM PFC rectifier with a low height of 5 mm has been realized and measurement results are provided in order to validate the theoretical considerations. The presented TCM PFC rectifier achieves an efficiency of 94.6% and a power factor of 99.3% at nominal power.
Detecting False Data Injection Attacks on DC State Estimation
(2010)

• Y. Liu, M. K. Reiter, and P. Ning. False Data Injection Attacks Against State Estimation in Electric Power Grids. In 16th ACM Conference on Computer and Communications Security (CCS ’09). Defense Against False Data Injection Attacks on DC State Estimation Miao Lu, University of Illinois at Urbana-Champaign ADVISORS: Rakesh Bobba, Kate Morrow I T I S U M M E R U N D E R G R A D U A T E I N T E R N P R O G R A M, 2 0 1 1
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
(2018)

Generative adversarial networks (GANs) are recently highly successful in generative applications involving images and start being applied to time series data. Here we describe EEG-GAN as a framework to generate electroencephalographic (EEG) brain signals. We introduce a modification to the improved training of Wasserstein GANs to stabilize training and investigate a range of architectural choices critical for time series generation (most notably upand down-sampling). For evaluation we consider and compare different metrics such as Inception score, Frechet inception distance and sliced Wasserstein distance, together showing that our EEG-GAN framework generated naturalistic EEG examples. It thus opens up a range of new generative application scenarios in the neuroscientific and neurological context, such as data augmentation in brain-computer interfacing tasks, EEG super-sampling, or restoration of corrupted data segments. The possibility to generate signals of a certain class and/or with specific properties may also open a new avenue for research into the underlying structure of brain signals.
Image inpainting
(2000)

Inpainting, the technique of modifying an image in an undetectable form, is as ancient as art itself. The goals and applications of inpainting are numerous, from the restoration of damaged paintings and photographs to the removal/replacement of selected objects. In this paper, we introduce a novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators. After the user selects the regions to be restored, the algorithm automatically fills-in these regions with information surrounding them. The fill-in is done in such a way that isophote lines arriving at the regions' boundaries are completed inside. In contrast with previous approaches, the technique here introduced does not require the user to specify where the novel information comes from. This is automatically done (and in a fast way), thereby allowing to simultaneously fill-in numerous regions containing completely different structures and surrounding backgrounds. In addition, no limitations are imposed on the topology of the region to be inpainted. Applications of this technique include the restoration of old photographs and damaged film; removal of superimposed text like dates, subtitles, or publicity; and the removal of entire objects from the image like microphones or wires in special effects.
An Introduction to Variational Methods for Graphical Models
(1999)

This paper presents a tutorial introduction to the use of variational methods for inference and learning in graphical models (Bayesian networks and Markov random fields). We present a number of examples of graphical models, including the QMR-DT database, the sigmoid belief network, the Boltzmann machine, and several variants of hidden Markov models, in which it is infeasible to run exact inference algorithms. We then introduce variational methods, which exploit laws of large numbers to transform the original graphical model into a simplified graphical model in which inference is efficient. Inference in the simpified model provides bounds on probabilities of interest in the original model. We describe a general framework for generating variational transformations based on convex duality. Finally we return to the examples and demonstrate how variational algorithms can be formulated in each case.
Texture Synthesis by Non-parametric Sampling
(1999)

A non-parametric method for texture synthesis is proposed. The texture synthesis process grows a new image outward from an initial seed, one pixel at a time. A Markov random field model is assumed, and the conditional distribution of a pixel given all its neighbors synthesized so far is estimated by querying the sample image and finding all similar neighborhoods. The degree of randomness is controlled by a single perceptually intuitive parameter. The method aims at preserving as much local structure as possible and produces good results for a wide variety of synthetic and real-world textures.
Nonlinear total variation based noise removal algorithms
(1992)

A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the gradient-projection method. This amounts to solving a time dependent partial differential equation on a manifold determined by the constraints. As t → ∞ the solution converges to a steady state which is the denoised image. The numerical algorithm is simple and relatively fast. The results appear to be state-of-the-art for very noisy images. The method is noninvasive, yielding sharp edges in the image. The technique could be interpreted as a first step of moving each level set of the image normal to itself with velocity equal to the curvature of the level set divided by the magnitude of the gradient of the image, and a second step which projects the image back onto the constraint set.
Google and the scholar: the role of Google in scientists' information-seeking behaviour
(2010)

Purpose – This paper aims to demonstrate the role that the Google general search engine plays in the information-seeking behaviour of scientists, particularly physicists and astronomers. Design/methodology/approach – The paper is based on a mixed-methods study including 56 semi-structured interviews, a questionnaire survey of 114 respondents (47 per cent response rate) and the use of information-event cards to collect critical incident data. The study was conducted at the Department of Physics and Astronomy at University College, London. Findings – The results show that Google is the tool most used for problem-specific information seeking. The results also show the growing reliance of scientists on general search engines, particularly Google, for finding scholarly articles. Initially, finding scholarly articles was a by-product of general searching for information rather than focused searches for papers. However, a growing number of articles read by scientists are identified through the Google general search engine and, as scientists are becoming more aware of the quantity of scholarly papers searchable by Google, they are increasingly relying on Google for finding scholarly literature. Research limitations/implications – As the only fields covered in the study were physics and astronomy, and the research participants were sourced from just one department of one institution, caution should be taken in generalising the findings. Originality/value – The data are based on a mixed-methods in-depth study of scientists’ information-seeking behaviour which sheds some light on a question raised in past studies relating to the reason for the high number of articles identified through Google.
Character-level incremental speech recognition with recurrent neural networks
(2016)

In real-time speech recognition applications, the latency is an important issue. We have developed a character-level incremental speech recognition (ISR) system that responds quickly even during the speech, where the hypotheses are gradually improved while the speaking proceeds. The algorithm employs a speech-to-character unidirectional recurrent neural network (RNN), which is end-to-end trained with connectionist temporal classification (CTC), and an RNN-based character-level language model (LM). The output values of the CTC-trained RNN are character-level probabilities, which are processed by beam search decoding. The RNN LM augments the decoding by providing long-term dependency information. We propose tree-based online beam search with additional depth-pruning, which enables the system to process infinitely long input speech with low latency. This system not only responds quickly on speech but also can dictate out-of-vocabulary (OOV) words according to pronunciation. The proposed model achieves the word error rate (WER) of 8.90% on the Wall Street Journal (WSJ) Nov'92 20K evaluation set when trained on the WSJ SI-284 training set.
A novel approach to CAD system for the detection of lung nodules in CT images
(2016)

Detection of pulmonary nodule plays a significant role in the diagnosis of lung cancer in early stage that improves the chances of survival of an individual. In this paper, a computer aided nodule detection method is proposed for the segmentation and detection of challenging nodules like juxtavascular and juxtapleural nodules. Lungs are segmented from computed tomography (CT) images using intensity thresholding; brief analysis of CT image histogram is done to select a suitable threshold value for better segmentation results. Simple morphological closing is used to include juxtapleural nodules in segmented lung regions. K-means clustering is applied for the initial detection and segmentation of potential nodules; shape specific morphological opening is implemented to refine segmentation outcomes. These segmented potential nodules are then divided into six groups on the basis of their thickness and percentage connectivity with lung walls. Grouping not only helped in improving system's efficiency but also reduced computational time, otherwise consumed in calculating and analyzing unnecessary features for all nodules. Different sets of 2D and 3D features are extracted from nodules in each group to eliminate false positives. Small size nodules are differentiated from false positives (FPs) on the basis of their salient features; sensitivity of the system for small nodules is 83.33%. SVM classifier is used for the classification of large nodules, for which the sensitivity of the proposed system is 93.8% applying 10-fold cross-validation. Receiver Operating Characteristic (ROC) curve is used for the analysis of CAD system. Overall sensitivity of the system is 91.65% with 3.19 FPs per case, and accuracy is 96.22%. The system took 3.8 seconds to analyze each image.
Optimal Online Cyberbullying Detection
(2018)

Cyberbullying has emerged as a serious societal and public health problem that demands accurate methods for the detection of cyber-bullying instances in an effort to mitigate the consequences. While techniques to automatically detect cyberbullying incidents have been developed, the scalability and timeliness of existing cyberbullying detection approaches have largely been ignored. We address this gap by formulating cyberbullying detection as a sequential hypothesis testing problem. Based on this formulation, we propose a novel algorithm designed to reduce the time to raise a cyberbullying alert by drastically reducing the number of feature evaluations necessary for a decision to be made. We demonstrate the effectiveness of our approach using a real-world dataset from Twitter, one of the top five networks with the highest percentage of users reporting cyberbullying instances. We show that our approach is highly scalable while not sacrificing accuracy for scalability.
Oral-motor function and feeding intervention.
(2013)

This article presents the elements of the Oral Motor Intervention section of the Infant Care Path for Physical Therapy in the Neonatal Intensive Care Unit (NICU). The types of physical therapy interventions presented in this path are evidence based as well as infant driven and family focused. In the context of anticipated maturation of suck-swallow-breathe coordination, the timing and methods for initiation of oral feedings and transition from gavage to full breast or bottle-feedings are presented with supporting evidence.
Transparent, lightweight application execution replay on commodity multiprocessor operating systems
(2010)

We present Scribe, the first system to provide transparent, low-overhead application record-replay and the ability to go live from replayed execution. Scribe introduces new lightweight operating system mechanisms, rendezvous and sync points, to efficiently record nondeterministic interactions such as related system calls, signals, and shared memory accesses. Rendezvous points make a partial ordering of execution based on system call dependencies sufficient for replay, avoiding the recording overhead of maintaining an exact execution ordering. Sync points convert asynchronous interactions that can occur at arbitrary times into synchronous events that are much easier to record and replay.
 We have implemented Scribe without changing, relinking, or recompiling applications, libraries, or operating system kernels, and without any specialized hardware support such as hardware performance counters. It works on commodity Linux operating systems, and commodity multi-core and multiprocessor hardware. Our results show for the first time that an operating system mechanism can correctly and transparently record and replay multi-process and multi-threaded applications on commodity multiprocessors. Scribe recording overhead is less than 2.5% for server applications including Apache and MySQL, and less than 15% for desktop applications including Firefox, Acrobat, OpenOffice, parallel kernel compilation, and movie playback.
The Jalview Java alignment editor
(2004)

Multiple sequence alignment remains a crucial method for understanding the function of groups of related nucleic acid and protein sequences. However, it is known that automatic multiple sequence alignments can often be improved by manual editing. Therefore, tools are needed to view and edit multiple sequence alignments. Due to growth in the sequence databases, multiple sequence alignments can often be large and difficult to view efficiently. The Jalview Java alignment editor is presented here, which enables fast viewing and editing of large multiple sequence alignments.